The ability to act quickly and decisively in today's increasingly competitive marketplace is critical to the success of any organization. The volume of data that is available to organizations is rapidly increasing and frequently overwhelming. The availability of large volumes of data presents various challenges. One challenge is to avoid inundating an individual with unnecessary information. Another challenge is to ensure all relevant information is available in a timely manner.
One known approach to addressing these and other challenges is known as data warehousing. Data warehouses, relational databases, and data marts are becoming important elements of many information delivery systems because they provide a central location where a reconciled version of data extracted from a wide variety of operational systems may be stored. As used herein, a data warehouse should be understood to be an informational database that stores shareable data from one or more operational databases of record, such as one or more transaction-based database systems. A data warehouse typically allows users to tap into a business's vast store of operational data to track and respond to business trends that facilitate forecasting and planning efforts. A data mart may be considered to be a type of data warehouse that focuses on a particular business segment.
Decision support systems have been developed to efficiently retrieve selected information from data warehouses. One type of decision support system is known as an on-line analytical processing system (“OLAP”). In general, OLAP systems analyze the data from a number of different perspectives and support complex analyses against large input data sets.
There are at least three different types of OLAP architectures—ROLAP, MOLAP, and HOLAP. ROLAP (“Relational On-Line Analytical Processing”) systems are systems that use a dynamic server connected to a relational database system. Multidimensional OLAP (“MOLAP”) utilizes a proprietary multidimensional database (“MDDB”) to provide OLAP analyses. The main premise of this architecture is that data must be stored multidimensionally to be viewed multidimensionally. A HOLAP (“Hybrid On-Line Analytical Processing”) system is a hybrid of these two.
Typically, business users rely on the above-noted OLAP systems to analyze large volumes of their business information in order to ascertain useful trends and productivity information. The OLAP systems are used to query databases containing the business information and to generate customizable reports which summarize this information.
While OLAP systems are a powerful tool for querying a business entities business information databases, the reports generated by these systems are not the preferred method of conveying information to other members of a business organization, in particular business managers and others who rely on this information to make business decisions. One reason for this, as noted above, is that interfacing with OLAP systems often requires technical expertise that is only possessed by relatively few individuals in a business organization. It is often necessary to learn a new programming interface in order to operate the OLAP system. Also, because OLAP systems are proprietary and relatively expensive, installation of OLAP clients is not universal among business employee computer systems. Generally, only those who have a need to interface with the OLAP system will have the OLAP client installed on their desktop computer. Another limitation of OLAP systems is that they typically have only limited formatting options available. As a result, reports generated by OLAP systems are frequently exported and used in other applications, such as, for example, business productivity clients whose installation and use is often more universal.
Business productivity clients, such as the MICROSOFT OFFICE suite of business applications are rapidly becoming the preferred method of retaining, visualizing and conveying business information. MICROSOFT OFFICE includes fundamental business applications including ACCESS database, EXCEL spreadsheet, MSWORD word processor and POWERPOINT presentation tool. These applications allow users to create sophisticated documents and visual presentations that transform raw business data into an aesthetically pleasing and meaningful format. As a result, their use in the business world has become nearly universal.
A drawback associated with exporting reports generated in an OLAP system to a business productivity client is that it is still necessary to learn the interfaces for both the OLAP client and the productivity client in order to run the report, export the data and merge it into a document in the productivity client. Moreover, this data, once merged into the productivity client, remains static. That is, if at a later data, the report from which the data was obtained is re-run, either to include new information or based on different parameters, this data must be once again taken from the report and imported or pasted into the document of the productivity client. Pasting the report data into a document of the productivity client is an inefficient process because it requires accessing both the OLAP client the productivity client. Moreover, the resulting document will likely include formatting from both the OLAP system as well as the productivity client which can lead to undesirable visual effects and even document corruption. Merging or exporting data from the OLAP system into the productivity client also requires accessing both the OLAP client and the productivity client. This too can cause undesirable and/or unintended changes to the resulting productivity client document, particularly, if the updated report is different than the one that it is replacing.
Therefore, these and other drawbacks exist with respect to conventional methods of bringing report data from an OLAP system into a document created in a business productivity client.